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Xabi Agirre, researcher at IDEKO and member of the Dynamics and Control team, has been awarded the Management Solutions 2026 Award for the Best Master’s Thesis in Digital Transformation for his work entitled “Dynamic Modeling, Control and Sizing of Feed Drives for Large-Scale Machine Tools.”
The award recognises the quality of a research project addressing one of the key technological challenges in the development of large-scale machine tools: understanding and optimising their dynamic behaviour to meet increasingly demanding requirements in terms of productivity, accuracy and manufacturing quality.
Understanding the dynamic behaviour of large-scale machine tools
Large-scale machine tools play a critical role in strategic sectors such as aerospace, energy and capital goods manufacturing. However, their high moving masses and reduced structural stiffness introduce inherent dynamic limitations that can constrain machine performance.
These limitations are often reflected in lower natural frequencies and increased sensitivity to vibration-related phenomena, particularly as industrial applications demand higher speeds, greater accelerations and more demanding motion profiles. In this context, dynamic behaviour becomes a key consideration from the earliest stages of machine design.
Xabi’s research focuses on the dynamic behaviour of feed drive systems and their interaction with the compliant machine structure, analysing how different design parameters influence system response and overall dynamic performance.
The importance of the load-to-motor inertia ratio
One of the central aspects of the study is the influence of the load-to-motor inertia ratio, a key parameter affecting both system stability and dynamic response.
To investigate its impact, the research combines time-domain and frequency-domain simulation approaches, providing a theoretical framework to evaluate how different configurations influence machine dynamics and the ability to execute high-precision movements.
The work also includes a sensitivity analysis based on a simplified dynamic model to assess the effect of parameters such as mass, stiffness and damping on machine behaviour. Particular attention is given to tool tip overshoot, a phenomenon closely linked to positioning accuracy and manufacturing quality.
Supporting feed drive sizing and design decisions
The results provide a solid framework for supporting decision-making during the early design stages of large-scale machine tools. In particular, the methodology contributes to feed drive sizing and parameter selection processes aimed at maximising dynamic performance.
This approach is especially valuable for machine tool manufacturers, as it enables potential dynamic limitations to be identified before the machine is built, helping optimise machine performance from the very beginning of the development process.
From Master’s Thesis to Research Career at IDEKO
Beyond the academic recognition, the award also reflects IDEKO’s commitment to developing new research talent linked to real industrial challenges.
The awarded work was carried out as part of Xabi’s university studies. After completing his degree, he joined IDEKO’s Dynamics and Control team, where he continues his research activity in the field of advanced manufacturing.
The award ceremony was held on 17 June, where Xabi was accompanied by Oier Franco, the IDEKO researcher who supervised and mentored the development of the project. This recognition further strengthens IDEKO’s commitment to knowledge generation, technology transfer and the development of future researchers capable of addressing the technological challenges facing advanced manufacturing industries.
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